Graph Signal Processing (GSP) based recommendation algorithms have recently attracted
lots of attention due to its high efficiency. However, these methods failed to consider the …

In the application recommendation field, collaborative filtering (CF) method is often
considered to be one of the most effective methods. As the basis of CF-based …

As most great discoveries and advancements in science and technology invariably involve
the cooperation of a group of researchers, effective collaboration is the key factor …

Collaborative Filtering (CF), a classical recommender system approach, learns users'
interests and behavioral preferences for items through a user–item interaction graph. CF …

Modern software development has moved toward agile growth and rapid delivery, where
developers must meet the changing needs of users instantaneously. In such a situation, plug …

Hashing-based recommender systems that represent users and items as binary hash codes
are recently proposed to significantly improve time and space efficiency. However, the highly …

现有的基于图神经网络的推荐模型在更新目标节点向量时大多对邻居节点信息进行无差别的
聚合, 没有结合推荐系统本身引入更多有用的先验知识, 从而区分目标节点与不同邻居节点之间 …

Abstract Knowledge Graph (KG) is widely used for recommendation tasks due to its rich
semantic information and external structure. Current knowledge graph recommendation …

Current study on next point-of-interest (POI) recommendation mainly explores user
sequential transitions with the fine-grained individual-user POI check-in trajectories only …

A recent study has shown that diffusion models are well-suited for modeling the generative
process of user--item interactions in recommender systems due to their denoising nature …